Autonomous mobile device, autonomous movement system, and autonomous movement method

ABSTRACT

An autonomous mobile device, an autonomous movement system, and an autonomous movement method, each having an obstacle avoidance capability, are provided. The autonomous mobile device includes an avoidance pattern determination unit for determining the travel pattern of the local device according to the state of motion, relative to the autonomous movement device, of a mobile obstacle other than the autonomous movement device; and a travel controller for causing the autonomous movement device to travel according to the travel pattern determined by the avoidance pattern determination unit. One of avoidance patterns is selected in accordance with a relative velocity to a mobile obstacle.

TECHNICAL FIELD

The present invention relates to technologies of an autonomous mobiledevice, an autonomous movement system, and an autonomous movementmethod, each having an obstacle avoidance capability.

BACKGROUND ART

Patent Document 1 discloses an autonomous mobile device having anobstacle avoidance capability. The technology disclosed in the PatentDocument 1 is that a region where entrance of the autonomous mobiledevice is restricted (an entry invalidating region) is set in a spacewhere an obstacle can move. The autonomous mobile device moves so as toavoid the entry invalidating region.

Patent Document 2 is also disclosed. Patent Document 2 discloses thatthe autonomous mobile device determines whether an obstacle is a humanbeing or not when detecting the obstacle. When the obstacle is a humanbeing, the autonomous mobile device stops and stands by for apredetermined period. When there is still the obstacle after elapse ofthe predetermined period, the autonomous mobile device shifts to anavoidance operation, and when there is no obstacle, restarts traveling.

PRIOR ART Patent Document

-   Patent Document 1: JP2009-157615 A-   Patent Document 2: JP 09-185412 A

SUMMARY OF INVENTION Problem to be Solved by Invention

If an autonomous mobile device moves on the same path facing a mobileobstacle autonomously moving, such as the human being, when theautonomous mobile device performs an avoidance behavior to avoid acollision with the mobile obstacle, both face each other again afteravoidance of the facing object in the same direction, and then may pausein a status of facing each other (hereinafter this status is referred toas same stepping). Such a same stepping frequently occurs in crowdedsituations at hospitals, stations, etc. Accordingly, at such places,there is a problem in that it takes a long time for the autonomousmobile device to reach a destination because the movement of theautonomous mobile device is remarkably interfered. Accordingly, there isa new problem for efficient movement by suppressing the same stepping.

For example, as described above, there is the technology disclosed inthe Patent Document 1 in which all regions to which the mobile obstaclemay move are set as the entry restricted area where the autonomousmobile device may enter. However, in this technology, the entryrestricted area tends to be set broadly because of no prediction of theavoidance behavior of the facing object. Accordingly, it becomesdifficult for the autonomous mobile device to move there because theadvancing course for the autonomous mobile device is closed by the entryrestricted areas. In other words, in the technology of Patent Document 1the course is closed by the entry restricted regions around the mobileobstacle under a crowded circumstance, so that the autonomous mobiledevice cannot move.

Accordingly, in the crowded environment or a narrow path, it isdesirable to perform avoidance only when necessary in accordance withbehavior of the facing object. As the method of performing avoidance ata necessary time, for example, there is a technology in which theautonomous mobile device waits for a predetermined period until thefacing object leaves when the obstacle is a mobile obstacle thatautonomously moves like the technology of Patent Document 2. However,when the facing object is a mobile obstacle having such a high motorability that the facing object can move speedier than the autonomousmobile device, the inventors confirmed that it is rather efficient towait until the facing object stands aside than avoidance of the facingobject by the autonomous mobile device. On the other hand, when thefacing object is a mobile obstacle having a poor motor ability such as aperson having difficulty in walking, it becomes inefficient when theautonomous mobile device waits until the facing object moves.Particularly, in the crowded environment such as hospitals, stations,etc., waiting frequently occurs until the facing object moves, so thatit takes long time for the autonomous mobile device to reach itsdestination. Accordingly, it is desirable to consider a motor ability ofthe facing object for the determination whether the avoidance isnecessary.

The present invention has been developed in consideration of theabove-described background and aims to decrease a frequency of the samestepping.

Means for Solving Problem

To solve the problem, the present invention is characterized byincluding: an avoidance pattern determining unit configured to determinewhether or not the autonomous mobile device oneself avoids a mobileobstacle which is different from the autonomous mobile device anddetermine one's own travelling pattern in accordance with a motionstatus of the mobile obstacle relative to the autonomous mobile device;and

a travelling control unit configured to cause the autonomous mobiledevice to travel in accordance with the travelling pattern determined bythe avoidance pattern determining unit.

Advantageous Effect of Invention

According to the present invention, the number of times of the samestepping can be reduced.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a functional block diagram of an autonomous movement systemaccording to a first embodiment.

FIGS. 2A and 2B are general views of an autonomous movement systemaccording to the first embodiment.

FIG. 3 is a flowchart illustrating a processing procedure of theautonomous mobile device according to the first embodiment.

FIGS. 4A to 4C are drawings showing examples of avoidance patterns.

FIGS. 5A and 5B are drawings showing a detailed method of calculating acollision margin.

FIG. 6 is a functional block diagram of the autonomous movement systemaccording to a second embodiment.

FIGS. 7A and 7B are general views of an autonomous movement systemaccording to the second embodiment.

FIG. 8 is a flowchart illustrating a processing procedure of theautonomous mobile device according to the second embodiment.

FIGS. 9A to 9C are drawings illustrating a detailed method ofcalculating a congestion factor according to the second embodiment.

FIGS. 10A and 10B are illustrations illustrating a detailed method ofacquiring a position and a velocity of the mobile obstacle according tothe second embodiment in which FIG. 10A shows a grid and FIG. 10B showsregions occupied by the obstacles assigned to the grids.

MODES FOR CARRYING OUT INVENTION

Next, modes for carrying out the invention (referred to as embodiments)are described with reference to drawings. In each of the drawings, thesame components are designated with the same references and theirdescription will be omitted.

First Embodiment

A first embodiment according to the present invention will be describedwith reference to FIGS. 1 to 5.

[Functional Block Diagram]

FIG. 1 is a functional block diagram of an autonomous movement systemaccording to the first embodiment.

An autonomous movement system Z includes a control unit 100, a datainput unit 2, an environment recognizing unit data input unit 200, and atravelling unit 30. Out of these units, the environment recognizing unit20 and the travelling unit 30 will be described with reference to FIGS.2A and 2B later.

The control unit 100 includes a memory 110, and a CPU (CentralProcessing Unit) 120. The respective programs of a controller 111, aself data estimating unit 112, an obstacle data estimating unit 113, acollision margin processing unit 114, an avoidance pattern determiningunit 115, and a travelling control unit 116 are spread in the memory110, and the CPU 120 executes the respective programs. Further, thememory 110 stores map data, data of a target position, one's ownposition, a travelling parameter of the autonomous mobile deviceoneself, and data of an obstacle.

The controller 111 controls the respective parts of 112 to 116. Further,the controller 111 controls the travelling unit 30 on the basis of datafrom the data input unit 2, the environment recognizing unit 20, and thetravelling unit 30.

A self data estimating unit 112 estimates one's own position and one'sown velocity. More specifically, the self data estimating unit 112calculates current coordinates, a current direction, an advancingvelocity, and a turning velocity of an autonomous mobile device 1 tostore the data in the memory 110,

The obstacle data estimating unit 113 estimates a position and avelocity of a mobile obstacle. More specifically, the obstacle dataestimating unit 113 calculates the position and the velocity of theobstacle on the basis of time history of the distance data points Ok(k=1, 2, 3, - - - , n: n being the number of shape point train obtainedby one time laser scanning) to the obstacle, stored in the memory 110from the environment recognizing unit 20. The obstacle data estimatingunit 113 estimates the velocity of the mobile obstacle by the methoddisclosed in, for example, JP2008-65755. In this method, the obstacledata estimating unit 113 detects rapidly changing point with respect toangle at a distance value obtained from a LIDAR (Laser Imaging Detectionand Ranging) 21 at time t. A data train is divided into respectivegroups of consecutive points as segments, which are stored in the memory110. More specifically, the obstacle data estimating unit 113 groups ashape point train on the basis of the rapidly changing points of thedistance value. The shape point train is grouped into a segment.

Accordingly, the obstacle data estimating unit 113 can recognizecharacteristic quantities such as representative centers of respectiveweight points and shapes of respective segments at time t. Next, theobstacle data estimating unit 113 performs calculation at time t+Δtsimilarly to acquire the characteristic quantities of respectivesegments. The obstacle data estimating unit 113 compares thecharacteristic quantities of the segments acquired at time t with thecharacteristic quantities of the segments acquired at time t+t andrecognizes segments having near values of the characteristic quantitiesas the same obstacle. Further, the obstacle data estimating unit 113estimates the velocity of the obstacle from a variation quantity of therepresentative position of respective segments recognized as the sameobstacle. The obstacle data estimating unit 113 presumes an obstaclehaving a travelling velocity of substantially “0” as a stationaryobstacle and other obstacle as mobile obstacles. Further, the obstacledata estimating unit 113 calculates velocities vck (vectors) of distancedata points Ok within the same segment and attaches the data and storesthe calculated velocities in the memory 110.

The collision margin processing unit 114 calculates a collision marginbetween the autonomous mobile device 1 and obstacles on the basis of thedata of oneself and the data of the mobile obstacles stored in thememory 110. Further, the collision margin processing unit 114quantitatively determines a margin of collision with mobile obstacle onthe basis of the calculated collision margins. Further, the collisionmargin processing unit 114 determines whether the current travellingstatus is maintained or emergency avoidance is performed on the basis ofthe collision margin. Calculation of the collision margin will bedescribed later with reference to FIG. 5. More specifically, thecollision margin is, for example, time up to collision of the autonomousmobile device 1 with the mobile obstacle.

The avoidance pattern determining unit 115 determines a countermeasure(avoidance pattern) on the basis of the velocity of the autonomousmobile device 1 oneself and the velocity of the mobile obstacle.

The travelling control unit 116 calculates control signals for providingthe travelling status and the avoidance pattern determined by theavoidance pattern determining unit 115 and transmits the result to thetravelling unit 30.

While the autonomous mobile device 1 is operating, the input data fromthe data input unit 2, the environment data from the environmentrecognizing unit 20, travelling data from the travelling unit 30, andcalculation results of the respective units 112 to 116 in the controlunit 100 are always inputted into the memory 110 for recoding andupdating.

The data input unit 2 is a device for inputting the map data and atarget point from the external.

The travelling unit 30 is a device for travelling the autonomous mobiledevice 1 and will be described later with reference to FIG. 2.

[General Views]

FIGS. 2A and 2B are general views of an autonomous movement systemaccording to the first embodiment. FIG. 2A is a front view of theautonomous mobile device and the data input unit 2. FIG. 2B is a sideview of the autonomous mobile device. In FIGS. 2A and 2B, the samecomponents as those in FIG. 1 are designated with the same referencesand their description are omitted.

The autonomous movement system Z includes the autonomous mobile device 1for performing the autonomous travelling and the data input unit 2 forinputting data into the autonomous mobile device 1 through radio waveLAN (Local Area Network) communication, etc. The data input unit 2 hasbeen described upon describing FIG. 1. Accordingly the description ofthe data input unit 2 in FIG. 2 is omitted.

As shown in FIG. 2, the autonomous mobile device 1 includes theenvironment recognizing unit 20, the control unit 100, motors 31,encoders 32, driving wheels 33, a free-direction type of caster 34, anda battery 40. Out of these units, the control unit 100 has beendescribed in detail upon describing FIG. 1. Accordingly, the descriptionof the control unit 100 is omitted.

The environment recognizing unit 20 is a device for recognizing otherobjects and geographical features which is the LIDAR (Laser ImagingDetection and Ranging) 21 in the first embodiment. The LIDAR 21 acquiresa data point Ok at a distance to an obstacle measured at a predeterminedregular angular interval as the environment data and stores the data asa time history recorded at each predetermined period. In the presentembodiment, when an obstacle is described simply as “obstacle”, theobstacle is a stationary obstacle and a mobile obstacle. When only anobstacle traveling is described, the obstacle is described as a mobileobstacle. A height of a scanning plane of the LIDAR 21 is preferablyabout 0.8 m from the ground so that the plane passes through near thepelvis of a human being. However, the height is not limited to this.

The motors 31 are installed on a bottom of the body of the autonomousmobile device 1 independently between right and left sides. The encoders32 measure rotational speeds of the left and right motors 31.

The driving wheels 33 are driven by the motors 31 to travel theautonomous mobile device 1 in forward and rearward directions.

The caster 34 can rotate in all directions and help the autonomousmobile device 1 to perform a turning operation.

The control unit 100 performs forward and turning operations for theautonomous mobile device 1 by independently control the rotationalspeeds of the left and right driving wheels 33. Current values of therotational speeds of the motors from the encoders 32 are alwaystransmitted to the control unit 100 in operation and stored as a timehistory of the motor rotational speeds for past several seconds in thememory 110.

The battery 40 is a unit for supplying an electric power to theenvironment recognizing unit (LIDAR) 20, the control unit 100,respective parts 31 to 34, etc.

[Flowchart]

FIG. 3 is a flowchart illustrating a processing procedure of theautonomous mobile device according to the first embodiment.

First, absolute coordinates or relative coordinates of a finaldestination is inputted to the autonomous mobile device 1 through thedata input unit 2. When a start of the operation of the autonomousmobile device 1 is instructed through the data input unit 2 at a giventiming, the following process is started.

When the process is started, the self data estimating unit 112calculates one's own position pr, one's own velocity vr, a position ofdistance data point (position of a mobile obstacle) po, and a velocityvc of the mobile obstacle (S101). The collision margin processing unit114 obtains from the memory 110 the one's own position pr, the velocityof the mobile obstacle vc, the position of distance data point (positionof the mobile obstacle) po, and the velocity of the mobile obstacle vc(S101). The references vc and vr are vectors. Hereinafter, it is assumedthat velocities other than the velocity of the mobile obstacle vc, andthe one's own velocity vr are also vectors.

Next, the collision margin processing unit 114 calculates a collisionmargin Mc (S102) and compares the calculated collision margin Mc withrespective thresholds Mmax, Mmin (Mmax>Mc>Mmin) (S103).

As the result of a step S103, when Mc Mmax, i.e., (S103→Mc Mmax), thecollision margin processing unit 114 determines that there is sufficientmargin up to a collision of the autonomous mobile device 1 with themobile obstacle, and maintains the current travelling status (S104), andprocessing proceeds to a step S111.

As the result of the step S103 when Mc Mmin, i.e., (S103→Mc Mmin), thecollision margin processing unit 114 determines that there is no marginup to the collision. Then, the collision margin processing unit 114causes the travelling control unit 116 to perform an emergency avoidance(S105), and then, processing proceeds to a step S111. The emergencyavoidance includes a stop, a rapid turning, etc. and which avoidanceoperation is to be made is previously set.

As the result of the step 103, when Mmin<Mc<Mmax, i.e.,(S103→Mmin<Mc<Mmax), the avoidance pattern determining unit 115 comparesthe velocity of the mobile obstacle ∥vc∥ with the one's own velocity∥vr∥ of the autonomous mobile device 1 (S106). The symbol ∥·∥ is a normof a vector. Further, in the below comparison, a relation betweendetermination thresholds α and β is β<0<α.

As the result of the step S106, when ∥vc∥−∥vr∥>α, i.e.,(S106→∥vc∥−∥vr∥>α), the avoidance pattern determining unit 115 cause thetravelling control unit 116 to perform an avoidance pattern A (S107),and the controller 111 proceeds to a process in a step S111. Theavoidance pattern A will be described later with reference to FIGS. 4Ato 4C.

As the result of the step S106, when ∥vc∥−∥vr∥<β, i.e.,(S106→∥vc∥−∥vr∥<β), the avoidance pattern determining unit 115calculates the collision margin Mc again (S108). The avoidance patterndetermining unit 115 causes the travelling control unit 116 to performan avoidance pattern B (S109) on the basis of the margin M calculatedagain, and the controller 111 proceeds to a process in the step S111.The avoidance pattern B is described latter with reference to FIG. 4.

As the result of the step S106, when β∥vc∥−∥vr∥α, i.e.,(S106→β∥vc∥−∥vr∥α), the avoidance pattern determining unit 115 causesthe travelling control unit 116 to perform an avoidance pattern C(S107), and then, the controller 111 proceeds to a process in a stepS111. The avoidance pattern C will be described later with reference toFIGS. 4A to 4C.

After steps S104, S105, S107, S109, and S110, the travelling controlunit 116 causes the autonomous mobile device 1 to travel in accordancewith the determined the avoidance pattern (S111).

The travelling control unit 116 determines whether travelling has beenfinished (S112). More specifically, the travelling control unit 116determines whether the autonomous mobile device 1 has reached the finaldestination point.

As the result of the step S112, when the travelling has not beencompleted (S112→No), the controller 111 returns to the process of thestep S101.

As the result of the step S112, when the travelling has been completed(S112→Yes), the controller 111 finishes the processing.

[Avoidance Pattern]

FIGS. 4A to 4C are drawings showing examples of avoidance patterns.These avoidance patterns are based on a tendency, found by theinventors, that a moving object having a higher velocity performs theavoidance at a higher probability. This is based on presumption that amoving object having a low velocity is probably an aged person or ahandicapped person.

In FIGS. 4A to 4C, as described above, “vc” is a velocity of the mobileobstacle and “vr” is a velocity of one's own velocity of the autonomousmobile device 1. Further, a reference 51 denotes a mobile obstacle, anda reference 52 denotes a stationary obstacle.

(∥vc∥−∥vr∥>α: Avoidance Pattern A)

FIG. 4A is a drawing showing an example of the avoidance pattern A, when∥vc∥−∥vr∥>α. In other words, FIG. 4A shows an example of the avoidancepattern A executed in the step S107 in FIG. 3.

The condition is ∥vc∥−∥vr∥>α means that ∥vc∥ is larger than ∥vr∥, andthe difference is larger than a high velocity determination threshold α.In this case, the avoidance pattern determining unit 115 estimates thata motor ability of the mobile obstacle is high.

Accordingly, the avoidance pattern determining unit 115 determines thatit has a higher probability that a mobile obstacle 51 previouslyperforms a travelling direction change to avoid a collision. In otherwords, the avoidance pattern determining unit 115 determines that thereis a probability that when the autonomous mobile device 1 performs thetravelling direction change, there is a high probability that theautonomous mobile device 1 do the same stepping because the travellingdirections are overlapped with the mobile obstacle 51 again. Then, theavoidance pattern determining unit 115 causes the travelling controlunit 116 to performs “maintaining the current motion direction andvelocity”, as shown in FIG. 4A as the avoidance pattern A.

More specifically, when the autonomous mobile device 1 faces the mobileobstacle 51 satisfying the condition of ∥vc∥−∥vr∥>α (i.e., the velocityis sufficiently higher than that of the mobile obstacle 51 oneself), theavoidance pattern determining unit 115 continues to maintain the currentmotion status (velocity and motion direction) until the collision marginMc becomes lower than Mmim. During this, when the mobile obstacle 51performs a travelling direction change, the collision can be avoided. Insuch a manner, the autonomous mobile device 1 and the mobile obstacle 51can rapidly pass by one another.

(∥vc∥−∥vr∥<β: the Avoidance Pattern B)

FIG. 4B is a drawing showing an example of the avoidance pattern B, when∥vc∥−∥vr∥<β. In other words, FIG. 4B shows an example of the avoidancepattern B executed in the step S109 in FIG. 3.

The condition is ∥vc∥−∥vr∥<β, which means that ∥vc∥ is smaller than∥vr∥, and the difference is larger than a low velocity determinationthreshold β. In this case, the avoidance pattern determining unit 115estimates that the motor ability of the mobile obstacle 51 is low.

Accordingly, the avoidance pattern determining unit 115 determines thatit has a low probability that the mobile obstacle 51 immediatelyperforms a travelling direction change to avoid a collision. In otherwords, the avoidance pattern determining unit 115 determines that whenthe autonomous mobile device 1 immediately performs the travellingdirection change, the autonomous mobile device 1 can avoid thecollision. Then, the avoidance pattern determining unit 115 causes acollision margin travelling control unit 116 to perform “a turn having alarger Mc calculated margin MC again in the step S109” as shown in FIG.5B.

Here, in the case that the avoidance pattern determining unit 115maintains a current motion direction and the current velocity, a regionwhere the autonomous mobile device 1 presumably passes up to thecollision with the mobile obstacle 51 is assumed as an estimated passingregion Ai (i being a natural number and a number being designated withan angle). The longer the estimated passing region Ai in the travellingdirection of the autonomous mobile device 1, the larger the collisionmargin Mc calculated again becomes. Then, the avoidance patterndetermining unit 115 determines that the estimated passing region withrespect to the current velocity is AO. The avoidance pattern determiningunit 115 checks the collision margins Mc at a constant angle interval inan order having a smaller turning angle in a left turning direction andthe right turning direction with a reference on AO. Next, the avoidancepattern determining unit 115 avoids the collision with the object bycausing the travelling control unit 116 to turn the autonomous mobiledevice 1 in a turning direction θi to the estimated passing region Axhaving a value of the collision margin Mc larger than the predeterminedconstant value.

(β∥vc∥−∥vr∥α: Avoidance Pattern C)

FIG. 4C is a drawing showing an example of the avoidance pattern C, whenβ∥vc∥−∥vr∥α. In other words, FIG. 4C shows an example of the avoidancepattern C executed in the step S110 in FIG. 3.

The condition is β∥vc∥−∥vr∥α, which means that a difference ∥vc∥−∥vr∥between ∥vc∥ and ∥vr∥ is larger than a low velocity determinationthreshold β and smaller than the high velocity determination thresholdα. In this case, the avoidance pattern determining unit 115 estimatesthat the motor ability of the mobile obstacle 51 is of the same degreeas the motor ability of the autonomous mobile device 1.

Accordingly, the avoidance pattern determining unit 115 causes thetravelling control unit 116 to perform “deceleration until the conditionbecomes ∥vc∥−∥vr∥>α as shown in FIG. 5C. More specifically, theavoidance pattern determining unit 115 decelerates the autonomous mobiledevice 1 to allow the velocity of the mobile obstacle 51 to berelatively faster.

In other words, the avoidance pattern determining unit 115 decelerateone's own velocity to a velocity vra providing a condition of∥vc∥−∥vra∥>α when the autonomous mobile device 1 faces the mobileobstacle 51 (having substantially the same velocity) having a conditionsatisfying β∥vc∥−∥vr∥α. This makes the velocity of the mobile obstacle51 relatively faster to forcibly generate the condition of the avoidancepattern A.

By performing the above-described operation, the autonomous mobiledevice 1 can transmit one's own intention of no travelling directionchange to the mobile obstacle 51, which urges the mobile obstacle 51 toperform avoidance by the travelling direction change. If the mobileobstacle 51 performs the travelling direction change to avoid thecollision before the collision margin Mc becomes lower than Mmin, whichis faster for the autonomous mobile device 1 than the case in which theautonomous mobile device 1 completely stops.

As described above, the motor ability of the mobile obstacle 51 isjudged with the travelling velocity of one's own velocity of theautonomous mobile device 1, determines whether the autonomous mobiledevice 1 should avoid the facing object in accordance with the needs inorder to avoid the same stepping and a collision due to avoiding thefacing object in the same directions, so that the autonomous mobiledevice 1 capable of efficiently moving can be provided.

[Details of Calculation of the Collision Margin]

FIGS. 5A and 5B are drawings showing a detailed method of calculating acollision margin.

The collision margin processing unit 114 (see FIG. 1) calculates amargin M on the basis of the the one's own position pr and velocity vr,a position pok and a velocity of distance data point Ok of the mobileobstacle (both being shown in FIG. 5A) stored in the memory 110 (seeFIG. 1). In the embodiment, the margin M is calculated on the basis oftime up to the collision when both continue the current motions. Thecollision margin processing unit 114 determines quantitatively whetherthere is a margin up to the collision with the mobile obstacle on thebasis of the collision margin. Here, “k” is a number in the shape pointtrain observed by the LIDAR (Laser Imaging Detection and Ranging) 21 asdescribed above. For example, M=(M1, M2, . . . , Mk, . . . ).

As shown in FIG. 5A, when it is assumed that a width, the position, andthe velocity of the autonomous mobile device 1 are wr, pr, and vr,respectively, there is a data point Ok (one of shape point trains of themobile obstacles) having a position pok and a velocity vck. In such acase, the collision margin processing unit 114 calculates using Eq. (1)a relative velocity vork and a normal vector nork of the relativevelocity vork of the autonomous mobile device 1 with respect to relativepositions prok, vck of the distance data point Ok relative to the prshown in FIG. 4B.prok=pok−prvork=vr−vck∥nork∥=1, and nork·vork=0  (1)

Here, when a distance sok from the distance data point Ok at vork in adirection of the normal is smaller than wr, there is a possibility thatthe autonomous mobile device 1 collides with the distance data point Ok.However, in FIGS. 4A to 4C, wr is set to be wider than the width of theautonomous mobile device 1 in consideration of safety. The collisionmargin processing unit 114 assumes that a time period up to thecollision is tk and calculates tk and sok by Eq. (2).

$\begin{matrix}{{{{Tk} = {{Vk}^{- 1}{prok}}}{where}{Tk} = \begin{bmatrix}{tk} \\{sok}\end{bmatrix}},{{Vk}^{- 1} = \begin{bmatrix}{vork} & {nork}\end{bmatrix}}} & (2)\end{matrix}$

Further, the collision margin processing unit 114 calculates a collisionmargin Mk from the calculated tk and sok as shown in Eq. (3).

$\begin{matrix}{{Mk} = \left\{ \begin{matrix}0 & {{tk} < {{t\mspace{14mu}\min}\bigcap{sk}} \leq {wr}} \\\frac{{tk} - {t\mspace{14mu}\min}}{{t\mspace{14mu}\max} - {t\mspace{14mu}\min}} & {{t\mspace{14mu}\min} \leq {tk} \leq {{t\mspace{14mu}\max}\bigcap{sk}} \leq {wr}} \\1 & {{tk} > {{t\mspace{14mu}\max}\bigcup{sk}} > {wr}}\end{matrix} \right.} & (3)\end{matrix}$

Previously set are tmin and tmax so as to be preferable in accordancewith the motion performance of the autonomous mobile device 1 and anenvironment, etc. For example, tmin is set to be time up to a stop ofthe autonomous mobile device 1 from the current velocity vr. Further,tmax is set as a period necessary for the autonomous mobile device 1 toreach a location having a maximum measurable distance of the LIDAR(Laser Imaging Detection and Ranging) 21 at one's own velocity vr.

The collision margin processing unit 114 calculates the collisionmargins Mk with all the distance data points Ok measured by the LIDAR(Laser Imaging Detection and Ranging) 21, and out of margins Mk, theminimum value is assumed to be the current final collision margin Mc.

The avoidance pattern determining unit 115 performs the process describeabove on the basis of the calculated collision margin Mc, the velocityof the mobile obstacle vc (a velocity in the direction Mc out of thevck), and the velocity vr of the autonomous mobile device 1 and selectsan optimal one is selected from a plurality of avoidance patterns(avoidance patterns A to C) set previously.

The travelling control unit 116 sends a driving voltage to the motor 31according to a specification of the motor 31 in accordance with theavoidance pattern stored in the memory 110.

Further, in the embodiment, the collision margin is defined as time.However, it is also possible that a distance is defined as the collisionmargin.

According to the first embodiment, because the avoidance pattern can bechanged in accordance with the velocity of the object facing one's ownof the autonomous mobile device 1 (mobile obstacle), so that anappropriate avoidance can be performed. Particularly, when the facingobject is faster than the autonomous mobile device 1 oneself,maintaining the one's own current moving status to have the facingobject avoided, which prevents from the traveling period to thedestination to extend.

Second Embodiment

Next, a second embodiment according to the present invention isdescribed with reference to FIGS. 6 to 10.

An autonomous movement system Za according to the second embodimentchanges a parameter (thresholds α, β) of determining the avoidancepattern in accordance with a congestion factor.

[Functional Block Diagram]

FIG. 6 is a functional block diagram of the autonomous movement systemaccording to the second embodiment.

The autonomous movement system Za includes a control unit 100 a, thedata input unit 2 a, the environment recognizing unit 20, and thetravelling unit 30.

Differences of the second embodiment from the autonomous movement systemZ according to the first embodiment are as follows:

-   (a1) Data of the environment recognizing unit 20 is inputted into a    data input unit 2 a.-   (a2) The respective programs of a control unit 211, a moving    environment analyzing unit 212, and an obstacle data estimating unit    213 are spread in a memory 210 of the data input unit 2 a, and a CPU    220 executes the respective programs. Processing results of the    moving environment analyzing unit 212 and the obstacle data    estimating unit 213 in the data input unit 2 a are inputted into the    control unit 100 a.-   (a3) In the first embodiment, the obstacle data estimating unit 213    provided in the control unit 100 is provided in the data input unit    2 a.-   (a4) A congestion factor processing unit 117 for performing a    process regarding the congestion factor is executed on a memory 110    a of the control unit 100 a.

Hereinafter, the data input unit 2 a and the congestion factorprocessing unit 117 are described. The controller 111, the self dataestimating unit 112, the collision margin processing unit 114, theavoidance pattern determining unit 115, the travelling control unit 116,and the travelling unit 30 in the control unit 100 a are the same asthose in the first embodiment, and thus their descriptions are omitted.

In the memory 210 of the data input unit 2 a, the programs of thecontrol unit 211, the moving environment analyzing unit 212, and theobstacle data estimating unit 213 are spread and the CPU 220 executesthe respective programs. Further, a time history of the environment datafor past several seconds inputted from the environment recognizing unit20 is stored in the memory 210.

The moving environment analyzing unit 212 calculates a ratio occupied bythe obstacles in a captured image on the basis of the data in the memory210 to calculate the congestion factor.

The obstacle data estimating unit 213 divides the obstacles intostationary obstacles and mobile obstacles by the method described above,and calculates and stores traveling velocities of the respectiveobstacles, etc. in the memory 110 a of the control unit 100 a.

In FIG. 6, the moving environment analyzing unit 212 and the obstacledata estimating unit 213 are provided in the data input unit 2 a, butmay be provided in the control unit 100 a. The data of the environmentrecognizing unit 20 may be directly inputted into the control unit 100a. In other words, calculation of the congestion factor, travelingvelocities of the obstacles, etc. may be performed in the control unit100 a.

The congestion factor processing unit 117 determines the threshold(described later) for determining the avoidance pattern in accordancewith the calculated congestion factor.

FIGS. 7A and 7B are general views of an autonomous movement systemaccording to the second embodiment. FIG. 7A shows a front view of theautonomous mobile device and the data input unit 2 a, and FIG. 7B showsonly a side view of the autonomous mobile device.

A difference of the autonomous movement system Za in FIGS. 7A and 7Bfrom the autonomous movement system Z in FIG. 3 is described as follows:

-   (b1) The environment recognizing unit 20 is not provided in an    autonomous mobile device 1 a, but installed in the environment as a    ceiling camera 21 a. An image captured by the ceiling camera 21 a is    inputted into the data input unit 2 a as the environment data.-   (b1) The autonomous mobile device 1 a includes a position indicator    50.

The motors 31, the encoders 32, the driving wheels 33, the caster 34,the battery 40 are the same as those in the first embodiment, and thustheir descriptions are omitted. Further, the data input unit 2 a hasbeen described regarding FIG. 6, and thus the description is omitted.Further, the control unit 100 a has been described regarding FIG. 6, andthus the description is omitted.

The ceiling camera 21 a as the environment recognizing unit 20 is acamera installed at a ceiling in the environment. Further, when themovable region is broad, a plurality of the ceiling cameras 21 a may beinstalled at a constant interval. Further, it is enough for the ceilingcamera 21 a to be installed at a position which allows the ceilingcamera 21 a to overlooks the environment, and for example, may have ashape to be hanged by a hanging means. Further, it is assumed that thethe ceiling camera 21 a is fixed, but may be movable by a mobile hangingmeans.

The image (picture) captured by the ceiling camera 21 a is transmittedto the data input unit 2 a by wires or radio wave communication. Thedata input unit 2 a analyzes a traveling environment from the image(picture) inputted from the ceiling camera 21 a (the environmentrecognizing unit 20) by a method described later with respect to FIG. 8and transmits the analysis result to the control unit 100 a through aradio wave LAN, etc.

The position indicator 50 is a device for indicating a position of theautonomous mobile device 1 relative to the circumference. A height ofthe position indicator 50 is about 1.0 m to match to a height of achild. However, the height is not limited to this height.

FIG. 8 is a flowchart illustrating a processing procedure of theautonomous mobile device according to the second embodiment. In FIG. 8,similar processes to those in FIG. 3 are designated with the same stepnumbers, and thus descriptions are omitted. Accordingly only differentprocesses are described.

First, when the process is started, the moving environment analyzingunit 212 calculates a congestion factor D (S201) and transmits thecongestion factor D to the control unit 100 a.

After that, the self data estimating unit 112 calculates the one's ownposition pr, the one's own velocity vr, the position of distance datapoint po, and the velocity of the mobile obstacle vc (S101).

As the result of the step S103, when Mmin<Mc<Mmax (S104→Mmin<Mc<Mmax),the congestion factor processing unit 117 determines the thresholds α,βon the basis of the calculated congestion factor D (S202).

For example, when the congestion factor D is smaller than a referencevalue Dmin (a reference value for determining that a moving environmentis sufficiently vacant), because it is not frequent that the samestepping occurs, the congestion factor processing unit 117 sets both theα,β to be larger. This allows the avoidance pattern B to be frequentlyselected.

Further, when the congestion factor D is larger than a reference valueDmax (a reference for determining that the moving environment isextremely crowded), the congestion factor processing unit 117 sets the βto be small because the turning of the autonomous mobile device 1 aremarkably blocks flows of other mobile obstacles. This allows theavoidance patterns A, C to be frequently selected.

After the step S202, the avoidance pattern determining unit 115 performsa process which is similar to the process from the step S107 to S112 inFIG. 3 using the determined threshold values α, β.

As described above, the autonomous mobile device 1 a can be provided todetermine the avoidance pattern in accordance with a selection referenceset in accordance with the congestion factor of the mobile environment.

[Calculate the Congestion Factor]

FIGS. 9A to 9C are drawings illustrating a detailed method ofcalculating a congestion factor according to the second embodiment.

First, the image much including a floor part as shown in FIG. 9Aselected from the captured images from a plurality of the ceilingcameras 21 a (see FIG. 6). The selection may be performed, for example,manually by the user. The moving environment analyzing unit 212generates histograms in intensities of RGB (Red Green Blue) as shown inFIG. 9B from the selected captured image. A color near respective peaksof RGB is estimated as a color of the floor. Then, the movingenvironment analyzing unit 212 determines that a floor color Qf isdefined by the RGB intensity satisfying Eq. (4) on the basis of thegenerated histograms.Qf={C|Rm−ΔRl<R<Rm+ΔRr∩Gm−ΔGl<G<Gm+ΔGr∩Bm−ΔBl<B<Bm+ΔBr}  (4)

When the environment is always constant in the Qf, the Qf may bepreviously extracted and stored in the memory 210 of the data input unitdata input unit 2 a and the memory 110 a of the autonomous mobile device1 a.

Next, the moving environment analyzing unit 212 extracts parts havingcolors other than the floor color Qf from a predetermined image capturedby the ceiling camera 21 a. As described above, the moving environmentanalyzing unit 212 extracts regions having colors other than the floorcolor Qf in the captured image as shown in FIG. 9C, i.e., objects otherthan the floor (that is, obstacles). The extracted obstacles includeboth the stationary obstacles and the mobile obstacles. The movingenvironment analyzing unit 212 calculates the congestion factor D of themoving environment quantitatively by calculating a ratio of the regionoccupied by the objects (obstacles) other than the floor within thecaptured image.

[Acquiring Method of Position and Velocity]

FIG. 10 shows views illustrating a detailed method of acquiring aposition and a velocity of the mobile obstacle according to the secondembodiment.

In the technology described in the first embodiment, the position andthe velocity of the mobile obstacle are calculated in which the shapepoint train is provided by the LIDAR 21 as a target. The technology ofthe second embodiment uses the ceiling camera 21 a. Accordingly, it isnecessary to calculate again the shape point train by the LIDAR 21.

Then, in the second embodiment, the moving environment analyzing unit212 classifies pixels in the captured image at time t into a floor andparts other than the floor using the method shown in FIGS. 9A to 9C.After that, the image classified into the floor and other part istransmitted to the control unit 100 a. The obstacle data estimating unit213 determines that adjoining pixels classified as the part other thanthe floor are of the same obstacle and divides the pixels into segmentsas shown in FIG. 10A. In other words, the obstacle data estimating unit213 performs grouping of the pixels to generate the segments.

The obstacle data estimating unit 213 extracts characteristic quantitiesof a representative position, a color, a size, etc. from the dividedsegment. The obstacle data estimating unit 213 stores the extractedcharacteristic quantities in the memory 110 a as a list. After that, theobstacle data estimating unit 213 performs the same process to imagecaptured after a minute time period of Δt seconds, determines that thepixels having the similar characteristic quantities as the same object,and obtains the velocity vc of the object from the variation quantity ofthe representative positions. The representative positions are, as shownby FIG. 10A, determined as the segment center po.

As shown in FIG. 10B, the obstacle data estimating unit 213 assignsregions occupied by the obstacles to the grids having a roughness tosuch an extent that the size is negligible, and provides velocities vck,positions pok to respective grids to be stored in the memory 110 a. Itis general that the velocities vck and the positions pok are those at acenter of the grid as shown in FIG. 10B, respectively, but not limitedto the center. The velocity vck, and the position pok correspond to vckand pok in FIG. 5, respectively. As described above, the collisionmargin processing unit 114 can calculate the collision margin by themethod described regarding FIG. 5.

As described above, the collision margin processing unit 114 cancalculate the collision margin by the same method as the firstembodiment. More specifically, the obstacle data estimating unit 213 canuse the same method as FIG. 5 by providing the velocity data andposition data to the grid as shown in FIG. 10B. The roughness of thegrids may be preferably set by the user in accordance with the size ortravelling performance of the autonomous mobile device 1 a.

According to the second embodiment, the thresholds for the avoidancepatterns are changed in accordance with the congestion factor, so thatthe avoidance pattern according to the environment can be selected.

Others

The autonomous mobile devices 1 and 1 a in the first and secondembodiments determines the avoidance patterns based on the actualvelocity of the mobile obstacle. The types of the moving objects may bedistinguished on the basis of the images captured by the camera. Furtherthe controller 111 of the autonomous mobile devices 1 and 1 a mayestimate the velocity of the facing object (motor ability) relative tooneself on the basis of the types of the moving object. The controller111 classified the facing mobile obstacle, into, for example, an agedperson having a stick, an injured person, a person pushing a carrier,and a person looking the other way, etc. The controller 111 determinesthat these persons have motor abilities which are lower than one's ownmotor ability. In other words, the controller 111 determines that thesepersons'velocities are slower than one's own velocity. On the otherhand, the controller 111 determines that, for example, a normal adultmale and a running person have high motor abilities. In other words, thecontroller 111 determines that a persons' velocities are faster thanone's own velocity.

As described above, for example, the autonomous mobile devices 1, 1 acan distinguish between a healthy person walking slowly, i.e., personhaving a high obstacle avoidance capability, and a person having a lowobstacle avoidance capability.

In addition, the autonomous mobile devices 1, 1 a change one's ownavoidance pattern in accordance with a motion state of the mobileobstacle. However, the technology described in Patent Document 1performs avoidance on the basis of the motion status which is notrelated to oneself and thus different from the technology described inthe embodiments of the invention.

DESCRIPTION OF REFERENCE SYMBOLS

-   1, 1 a autonomous mobile device-   2, 2 a data input unit-   20 environment recognizing unit-   21 LIDAR laser imaging detection and Ranging)-   21 a ceiling camera-   30 travelling unit-   31 motor-   32 encoder-   33 driving wheel-   34 caster-   40 battery-   100, 100 a control unit-   100 memory-   111 controller-   112 self data estimating unit-   113 obstacle data estimating unit-   114 collision margin processing unit-   115 avoidance pattern determining unit-   116 travelling control unit-   117 congestion factor processing unit-   210 memory-   20 control unit-   212 moving environment analyzing unit-   213 obstacle data estimating unit-   Z, Za autonomous movement system

The invention claimed is:
 1. An autonomous mobile device comprising: anenvironment recognizing unit configured to recognize a plurality ofmobile obstacles, including a first mobile obstacle, an avoidancepattern determining unit configured to determine an avoidancepattern-and determine a travelling pattern for the autonomous mobiledevice in accordance with a motion status of the first mobile obstaclerelative to the autonomous mobile device; a travelling control unitconfigured to cause the autonomous mobile device to travel in accordancewith the travelling pattern determined by the avoidance patterndetermining unit; wherein the avoidance pattern determining unitdetermines the travelling pattern comprises maintaining a motiondirection of the autonomous mobile device when a value obtained bysubtracting a velocity of the autonomous mobile device from a velocityof the mobile obstacle is equal to or greater than a first value; andthe first value is determined in accordance with a congestion factor ofthe plurality of the mobile obstacles.
 2. The autonomous mobile deviceas claimed in claim 1, wherein the avoidance pattern determining unitdetermines the travelling pattern comprises changing direction of theautonomous mobile device when the value obtained by subtracting thevelocity of the autonomous mobile device from the velocity of the firstmobile obstacle is less than a second value, and wherein the first valueis greater than the second value.
 3. The autonomous mobile device asclaimed in claim 2, wherein the second value is determined in accordancewith the congestion factor of the plurality of the mobile obstacles. 4.The autonomous mobile device as claimed in claim 1, wherein theavoidance pattern determining unit determines the travelling patterncomprises providing deceleration of the autonomous mobile device whenthe value obtained by subtracting the velocity of the autonomous mobiledevice from the velocity of the first mobile obstacle is within apredetermined range.
 5. The autonomous mobile device as claimed in claim1, wherein the avoidance pattern determining unit determines thetravelling pattern comprises providing deceleration of the autonomousmobile device when the value obtained by subtracting the velocity of theautonomous mobile device from the velocity of the first mobile obstacleis smaller than the first value and larger than a second value.
 6. Theautonomous mobile device as claimed in claim 5, wherein the second valueis determined in accordance with the congestion factor of the pluralityof the mobile obstacles.
 7. The autonomous mobile device as claimed inclaim 1, wherein the environment recognizing unit comprises a cameracapturing a front view, wherein the avoidance pattern determining unitestimates the motion status of the first mobile obstacle in accordancewith a type of the first mobile obstacle in an image captured by thecamera.
 8. An autonomous mobile system comprising: an environmentrecognizing unit configured to recognize a plurality of mobileobstacles, including a first mobile obstacle, an autonomous mobiledevice having an avoidance pattern determining unit configured todetermine an avoidance pattern and determine a travelling pattern forthe autonomous mobile device in accordance with a motion status of thefirst mobile obstacle relative to the autonomous mobile device; and atravelling control unit configured to cause the autonomous mobile deviceto travel in accordance with the travelling pattern determined by theavoidance pattern determining unit; wherein the avoidance patterndetermining unit determines the avoidance pattern comprises maintaininga motion direction of the autonomous mobile device when a value obtainedby subtracting a velocity of the autonomous mobile device from avelocity of the first mobile obstacle is equal to or greater than afirst value; and the first value is determined in accordance with acongestion factor of the plurality of the mobile obstacles.
 9. Theautonomous mobile system as claimed in claim 8, further comprising adata processing unit configured to calculate the congestion factor ofthe plurality of mobile obstacles, wherein the autonomous mobile devicechanges a reference of a motion status in accordance with the congestionfactor calculated by the data processing unit and determines thetravelling pattern on the basis of the changed reference.
 10. Anautonomous movement method by an autonomous mobile device performing anautonomous travelling comprising: recognizing, by an environmentrecognizing unit, a plurality of mobile obstacles, including a firstmobile obstacle, determining whether or not the autonomous mobile deviceavoids the first mobile obstacle; determining a travelling pattern ofthe autonomous mobile device in accordance with a motion status of thefirst mobile obstacle relative to the autonomous mobile device; andcausing the autonomous mobile device to travel in accordance with thedetermined travelling pattern; wherein the travelling pattern comprisesmaintaining a motion direction of the autonomous mobile device when avalue obtained by subtracting a velocity of the autonomous mobile devicefrom a velocity of the first mobile obstacle is equal to or greater thana first value; and the first value is determined in accordance with acongestion factor of the plurality of mobile obstacles.